International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438 Volume 4 Issue 1, January 2015 www.ijsr.net Licensed Under Creative Commons Attribution CC BY Face Recognition and Detection using Viola-Jones and Cross Correlation Method Ranjeet Singh 1 , Mandeep Kaur 2 1, 2 Guru Kashi Universiy, Sardulgarh Road, Talwandi Sabo, Punjab 151302, India Abstract: The face detection is process of detecting region of face from a picture of one or multiple persons together. The detected face is extracted in the proposed using the viola-Jones algorithm. The viola-Jones algorithm is considered effective in order to mark and extract the face features. The proposed model is using the correlation model for the purpose of the face recognition. The face recognition process can detect the person among the database of faces without knowing any other details about the person specific. The proposed face detection and recognition model can be deployed anywhere it is required. The results have shown the effectiveness of the proposed model. Keywords: Viola Jones, correlation, face detection, face recognition 1. Introduction Face detection is a technique what refer to the detection of the face automatically by digital camera. Face Recognition is a term used for recognition of a person automatically by computerized systems by taking a look at his/her face. Face detection is a popular feature used in biometrics, digital cameras and social tagging. Face detection and recognition has gained more research attentions in last some years. There are many good uses of this face detection and recognition feature. It can be used as biometric authentication. It can be used in digital camera for best picture contrast. It can be used for social tagging. Biometric systems are the automatic methods of recognizing a person based on a physiological or behavioral characteristic. Major authentication methods used are as following: Like passwords, PIN, smart card, token or card key, finger print, finger vein. Face detection is an almost unique biometric identity. There are very few chances of having two similar faces. So it can be used in the biometric identity based authentications systems. For security hardening it can used in combination with smart card or key card. Face detection is very important feature in digital cameras and social tagging. In digital cameras, Face detection is used because it controls the contrast on face in the clicked picture and can also help to view the clearer face than the click without face detection. In social tagging, face tagging is used to tag the people in the picture or post. In existing face detection algorithms, various face detection algorithm methods use various face detection methods like knowledge-based method, feature invariant approaches, template matching method and appearance based methods. In this proposed algorithm we are using template matching face detection method. Knowledge based methods uses the already programmed characteristics to detection the face, whereas appearance based method learn the face shapes by reading various training templates. Feature invariant method uses the object features for the feature detection in an image. Template based method uses the active template comparison, which provide the most accurate results in case of face detection. Face recognition is used in many applications such as security systems, credit card verification and criminal identification. Due to numerous potential applications face recognition has become a very active research area. In surveillance system if an unknown face appears more than one time then it is stored in database for further recognition. In general, face recognition techniques can be divided into two groups based on the face representation they use appearance-based, which uses holistic texture features and is applied to either whole-face or specific regions in a face image and feature-based, which uses geometric facial features (mouth, eyes, brows, cheeks etc), and geometric relationships between them. Face recognition is the art that compares the similarities of a face under test and the database image based on biometric features that are constant throughout the life of an individual irrespective of age and environmental conditions. In signal processing or image processing, there are a number of methods for template matching are used for various purposes. In example of Google image search, the algorithm used is a image template matching algorithm. In speaker detection application, there are various voice template matching algorithms are used for various properties of voice. All of these template matching techniques consist of various small feature code segments. These feature code segments may offer noise reduction, light normalization, computer vision anti blurring, feature extraction, feature analysis or feature detection. Out of these all template matching features, the popular among all is cross correlation and there are various cross correlation algorithms used for the template matching. There are normalized cross-correlation and generalized cross-correlation. Normalized cross- correlation for image-processing applications in which the brightness of the image and template can vary due to lighting and exposure conditions, the images can be first normalized. This is typically done at every step by subtracting the mean and dividing by the standard deviation. Image cross-correlation compares two image matrices based on various mathematical techniques. Cross correlation in images can be based upon various image characteristics like color patterns, color pixels, matrix coordinates, etc. Paper ID: SUB15808 2498
4
Embed
Face Recognition and Detection using Viola-Jones and … · The viola-Jones algorithm is considered effective in order to mark and extract the face features. ... systems are the automatic
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438